Early Tumor Diagnosis in Brain MR Images via Deep Convolutional Neural Network Model

Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s inspection.However,the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory,clinical mapping,and delusion tracing.In this proposed study,a deep learning based framework that employs deep convolution neural network(Deep-CNN),by utilizing both clinical presentations and conventional magnetic resonance imaging(MRI)investigations,for diagnosing tumors is explored.This research aims to develop a model that can be used for abnormality detection over MRI data quite efficiently with high accuracy.This research is based on deep learning and Deep-CNN was deployed to examine the MR brain image for tracing the tumor.The system runs on Tensor flow and uses a feature extraction module in DeepCNN to elicit the factors of that part of the image from where underlying issues are identified and subsequently succeeded in prediction of the disease in the MR image.The results of this study showed that our model did not have any adverse effect on classification,achieved higher accuracy than the peers in recent years,and attained good detection outcomes including case of abnormality.In the future work,further improvement can be made by designing models that can drastically reduce the parameter space without affecting classification accuracy.

PANCREATO-BILIARY TUMOR: MR DIAGNOSIS AND RESECTABILITY ASSESSMENT

Objective: TO evaluate the clinical value of MR multi-imaging technique in diagnosing and assessing the resectability of pancreato-biliary tumor. Methods The prospective diagnosis and resectability of 17 patients with suspicious pancreato-biliary tumors were evaluated. Surgical findings and pathologic results confirmed pancreatic adenocarcinoma in 11 cases, cholangiocarcinoma in 4, and non-neoplastic lesion in 2. MR multi-imaging protocol, including MR cross-sectional imaging, us cholangiopancreatography (MRCP ), and three-dimensional dynamic contrast-enhanced MR portography (3D DCE MRP), were performed in all patients. Results MR multi-imaging technique allowed-correct diagnosing 15 of 17 (88. 2% ) patients with pancreato-biliary tumors. The accuracy in detecting the range of tumor invasion was 64. 4%. The sensitivity, speificity, accuracy, positive, and negative predictive value of MR multi-imaging technique in assessing the resectability of pancreato-biliary tumors were 83. 3%, 77. 8%, 80. 0%, 71. 4%, and 87. 5%, respectively. Conclusion MR multi-imaging technique can not only improve the diagnostic ability of pancreato-biliary tumor, but also assess the surgical reartability of the tumor. With the fast development of MR techniques, the diagnosing and pre-operative assessment of aoncreato-biliary tumor may be more simplified and efficient by using the non-invasive "all-in-one" method.

Pancreatic Neuroendocrine Neoplasms: Correlation between MR Features and Pathological Tumor Grades

This study investigated the accuracy of MRI features in differentiating the pathological grades of pancreatic neuroendocrine neoplasms（PNENs）. A total of 31 PNENs patients were retrospectively evaluated, including 19 cases in grade 1, 5 in grade 2, and 7 in grade 3. Plain and contrastenhanced MRI was performed on all patients. MRI features including tumor size, margin, signal intensity, enhancement patterns, degenerative changes, duct dilatation and metastasis were analyzed. Chi square tests, Fisher＇s exact tests, one-way ANOVA and ROC analysis were conducted to assess the associations between MRI features and different tumor grades. It was found that patients with older age, tumors with higher TNM stage and without hormonal syndrome had higher grade of PNETs（all P〈0.05）. Tumor size, shape, margin and growth pattern, tumor pattern, pancreatic and bile duct dilatation and presence of lymphatic and distant metastasis as well as MR enhancement pattern and tumor-topancreas contrast during arterial phase were the key features differentiating tumors of all grades（all P〈0.05）. ROC analysis revealed that the tumor size with threshold of 2.8 cm, irregular shape, pancreatic duct dilatation and lymphadenopathy showed satisfactory sensitivity and specificity in distinguishing grade 3 from grade 1 and grade 2 tumors. Features of peripancreatic tissue or vascular invasion, and distant metastasis showed high specificity but relatively low sensitivity. In conclusion, larger size, poorlydefined margin, heterogeneous enhanced pattern during arterial phase, duct dilatation and the presence of metastases are common features of higher grade PNENs. Plain and contrast-enhanced MRI provides the ability to differentiate tumors with different pathological grades.

Asymptomatic pancreatic lesions: New insights and clinical implications

Despite great efforts in experimental and clinical research, the prognosis of pancreatic cancer (PC) has not changed significantly for decades. Detection of pre-invasive lesions or early-stage PC with small resectable cancers in asymptomatic individuals remains one of the most promising approaches to substantially improve the overall outcome of PC. Therefore, screening programs have been proposed to identify curable lesions especially in individuals with a familial or genetic predisposition for PC. In this regard, Canto et al recently contributed an important article comparing computed tomography, magnetic resonance imaging, and endoscopic ultrasound for the screening of 216 asymptomatic high-risk individuals (HRI). Pancreatic lesions were detected in 92 of 216 asymptomatic HRI (42.6%). The high diagnostic yield in this study raises several questions that need to be answered of which two will be discussed in detail in this commentary: First: which imaging test should be performed? Second and most importantly: what are we doing with incidentally detected pancreatic lesions? Which ones can be observed and which ones need to be resected?

A Survey of MRI-Based Brain Tumor Segmentation Methods

Brain tumor segmentation aims to separate the different tumor tissues such as active cells, necrotic core,and edema from normal brain tissues of White Matter（WM）, Gray Matter（GM）, and Cerebrospinal Fluid（CSF）. MRIbased brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging（MRI） images. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting brain tumor are becoming more and more mature and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for MRI-based brain tumor segmentation methods. Firstly, a brief introduction to brain tumors and imaging modalities of brain tumors is given. Then, the preprocessing operations and the state of the art methods of MRI-based brain tumor segmentation are introduced. Moreover, the evaluation and validation of the results of MRI-based brain tumor segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for MRI-based brain tumor segmentation methods.

Real-time monitoring of tumor vascular disruption induced by radiofrequency assisted gadofullerene

The anti-vascular therapy has been extensively studied for high performance tumor therapy by suppressing the tumor angiogenesis or cutting off the existing tumor vasculature. We have previously reported a novel anti-tumor treatment technique using radiofrequency （RF）-assisted ga- dofullerene nanocrystals （GFNCs） to selectively disrupt the tumor vasculature. In this work, we further revealed the changes on morphology and functionality of the tumor vas-culature during the high-performance RF-assisted GFNCs treatment in vivo. Here, a dearly evident mechanism of this technique in tumor vascular disruption was elucidated. Based on the H22 tumor bearing mice with dorsal skin flap chamber （DSFC） mode] and the dynamic contrast enhanced magnetic resonance imaging （DCE-MRI） technique, it was revealed that the GFNCs would selectively inset in the gaps of tumor vas-culature due to the innately incomplete structures and unique microenvironment of tumor vasculature,＇ and they damaged the surrounding endothelia cells excited by the RF to induce a phase transition accompanying with size expansion. Soon afterwards, the blood flow of the tumor blood vessels was permanently shut off, causing the entire tumor vascular net- work to collapse within 24 h after the treatment. The RF-as- sistant GFNCs technique was proved to aim at the tumor vasculatnre precisely, and was harmless to the normal vascu- lature. The current studies provide a rational explanation on the high efficiency anticancer activity of the RF-assisted GFNCs treatment, suggesting a novel technique with potent clinical application.